caret v5.05.004

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by Max Kuhn

Classification and Regression Training

Misc functions for training and plotting classification and regression models

Functions in caret

Name Description
BloodBrain Blood Brain Barrier Data
aucRoc Compute the area under an ROC curve
bag.default A General Framework For Bagging
caret-internal Internal Functions
bagFDA Bagged FDA
bagEarth Bagged Earth
cars Kelly Blue Book resale data for 2005 model year GM cars
avNNet.default Neural Networks Using Model Averaging
classDist Compute and predict the distances to class centroids
BoxCoxTrans.default Box-Cox Transformations
confusionMatrix Create a confusion matrix
diff.resamples Inferential Assessments About Model Performance
cox2 COX-2 Activity Data
confusionMatrix.train Estimate a Resampled Confusion Matrix
GermanCredit German Credit Data
dhfr Dihydrofolate Reductase Inhibitors Data
featurePlot Wrapper for Lattice Plotting of Predictor Variables
dotPlot Create a dotplot of variable importance values
filterVarImp Calculation of filter-based variable importance
findCorrelation Determine highly correlated variables
findLinearCombos Determine linear combinations in a matrix
Alternate Affy Gene Expression Summary Methods. Generate Expression Values from Probes
format.bagEarth Format 'bagEarth' objects
knn3 k-Nearest Neighbour Classification
icr.formula Independent Component Regression
predict.train Extract predictions and class probabilities from train objects
dotplot.diff.resamples Lattice Functions for Visualizing Resampling Differences
knnreg k-Nearest Neighbour Regression
xyplot.resamples Lattice Functions for Visualizing Resampling Results
mdrr Multidrug Resistance Reversal (MDRR) Agent Data
modelLookup Descriptions Of Models Available in train()
dummyVars Create A Full Set of Dummy Variables
nearZeroVar Identification of near zero variance predictors
oil Fatty acid composition of commercial oils
normalize.AffyBatch.normalize2Reference Quantile Normalization to a Reference Distribution
normalize2Reference Quantile Normalize Columns of a Matrix Based on a Reference Distribution
nullModel Fit a simple, non-informative model
maxDissim Maximum Dissimilarity Sampling
panel.needle Needle Plot Lattice Panel
plot.varImp.train Plotting variable importance measures
createGrid Tuning Parameter Grid
plot.train Plot Method for the train Class
pcaNNet.default Neural Networks with a Principal Component Step
plotClassProbs Plot Predicted Probabilities in Classification Models
plsda Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
pottery Pottery from Pre-Classical Sites in Italy
histogram.train Lattice functions for plotting resampling results
postResample Calculates performance across resamples
lift Lift Plot
predictors List predictors used in the model
plotObsVsPred Plot Observed versus Predicted Results in Regression and Classification Models
prcomp.resamples Principal Components Analysis of Resampling Results
preProcess Pre-Processing of Predictors
predict.knn3 Predictions from k-Nearest Neighbors
predict.bagEarth Predicted values based on bagged Earth and FDA models
predict.knnreg Predictions from k-Nearest Neighbors Regression Model
resampleHist Plot the resampling distribution of the model statistics
print.train Print Method for the train Class
resampleSummary Summary of resampled performance estimates
print.confusionMatrix Print method for confusionMatrix
rfeControl Controlling the Feature Selection Algorithms
roc Compute the points for an ROC curve
rfe Backwards Feature Selection
caretFuncs Backwards Feature Selection Helper Functions
caretSBF Selection By Filtering (SBF) Helper Functions
sbfControl Control Object for Selection By Filtering (SBF)
sbf Selection By Filtering (SBF)
segmentationData Cell Body Segmentation
oneSE Selecting tuning Parameters
sensitivity Calculate sensitivity, specificity and predictive values
spatialSign Compute the multivariate spatial sign
tecator Fat, Water and Protein Content of Meat Samples
trainControl Control parameters for train
varImp Calculation of variable importance for regression and classification models
resamples Collation and Visualization of Resampling Results
summary.bagEarth Summarize a bagged earth or FDA fit
as.table.confusionMatrix Save Confusion Table Results
train Fit Predictive Models over Different Tuning Parameters
lattice.rfe Lattice functions for plotting resampling results of recursive feature selection
panel.lift2 Lattice Panel Functions for Lift Plots
createDataPartition Data Splitting functions
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